The present invention relates to the detection of calcification and the segmentation of vessel lumen and vessel walls in three-dimensional volume data sets obtained by medical imaging procedures such as computed tomography (CT).
CT scanning and other three-dimensional patient imaging techniques such as magnetic resonance imaging yield large image data sets comprised of three-dimensional arrays of voxels. Each voxel has a value indicative of the tissue type present at the corresponding location in the patient's body. In a CT data set the voxels have values defined in Hounsfield units (HU), representing the density of the tissue to the X-rays used to record the image.
When analysing the data set for research or diagnostic purposes, it is necessary to identify which voxels correspond to which types of tissue, so the voxels can be classified according to tissue type. Different organs and tissues can then be distinguished in the image. Subsequently, it is often desirable to be able to separate the voxels classified as being of a particular tissue type or organ from the remainder of the data set. This is known as segmentation. For example, in the procedure known as computed tomographic angiography (CTA), the blood vessels are of interest, so these need to be segmented from the data set as a whole. More specifically, coronary CTA records the coronary arteries, so these are segmented out of the data set. Furthermore, a coronary artery (in common with all blood vessels) is comprised of the vessel wall, being the various tissues of the wall itself including any deposits of plaque, and the lumen, being the blood-filled space bounded by the wall. Segmentation of both the lumen and the wall can be used to identify diseased regions of the vessel and is a diagnostic aid in cases of coronary artery disease. For example, accurate segmentation of lumen and wall is required in order to perform stenosis measurements, which can be part of a wider structural analysis of the coronary arteries imaged with CTA that aims to identify obstructions to vessel blood flow. Many automated techniques are available for segmentation, based on established values of HU intensity for different tissue types which enable a computer program to distinguish between voxels representing the lumen and voxels representing the vessel wall.
For success in these procedures, it is important to properly identify any deposits of calcium within the vessels. This identification can be difficult in coronary CTA images that are obtained in a contrast CTA scan, in which contrast dye is injected into a vein of the patient, and images are recorded as the dye circulates into the arteries. Calcium deposits tend to have a very similar HU intensity to that of the contrasted blood in the lumen. Hence it is difficult to tell calcium voxels apart from lumen voxels, which makes accurate segmentation difficult. For example, there are techniques that rely on a fixed HU threshold marking the boundary between calcium and blood to identify calcium deposits in an image and which operate satisfactorily to segment non-contrast enhanced CTA image data sets. This approach works less well in contrast CTA data, because there is no clear threshold between calcium and blood that can be selected in advance.